Overwatch: An Educational Testbed for Multi-Robot Experimentation.

Abstract

Educators who wish to engage their students in multiagent experimentation and learning need an inexpensive multi-robot system that leverages existing equipment and open-source software. This paper proposes Overwatch as an inexpensive educational tool for teaching and experimenting in multi-robot systems. The interaction of multiple agents within a single environment is an important area of study. It is vital that agents within the environment perceive other agents as intelligent, acting within the environment as cooperative teammates or as competitive members of another team. To do so, the system must meet three goals: first, to allow multiple robots to communicate and coordinate; second, to localize within a shared global coordinate system; third, to recognize their teammates and other teams. The cost and scale of such experimental platforms places them outside the reach of many educational institutions or limits the number of agents that are interacting within the system (Liu and Winfield 2011). The goal of Overwatch is to create an experimental platform for multiagent systems that is comprised of much smaller, albeit less capable, robots, many of which are prevalent in academic institutions already. Making use of available open-source libraries and utilizing lower cost robots, such as Scribblers, allows for experiments with many agents. This enables Overwatch to fit into the budget limitations of an academic setting. The Overwatch platform provides the Scribblers with global localization capabilities. This paper presents the system in detail and includes experiments to show its ability to localize, interact with other agents, and coordinate behaviors with these other agents. Additionally, the details to setup this system are also included.